Title: Intrusion Detection Using Transfer Learning in Machine Learning Classifiers Between Non-cloud and Cloud Datasets
Authors: Roja Ahmadi, Robert D. Macredie and Allan Tucker
Abstract: One of the critical issues in developing intrusion detection systems (IDS) in cloud-computing environments is the lack of publicly available cloud intrusion detection datasets, which hinders research into IDS in this area. There are, however, many non-cloud intrusion detection datasets. This paper seeks to leverage one of the well-established non-cloud datasets and analyze it in relation to one of the few available cloud datasets to develop a detection model using a machine learning technique. A complication is that these datasets often have different structures, contain different features and contain different, though overlapping, types of attack. The aim of this paper is to explore whether a simple machine learning classifier containing a small common feature set trained using a non-cloud dataset that has a packet-based structure can be usefully applied to detect specific attacks in the cloud dataset, which contains timebased traffic. Through this, the differences and similarities between attacks in the cloud and non-cloud datasets are analyzed and suggestions for future work are presented.
Conference: The 19th International conference on Intelligent Data Engineering and Automated Learning (IDEAL 2018), Madrid, Spain.
The paper will publish in Springer LNCS/LNAI Proceedings.